class_docstr: Test the risk of disqualifying for fair use via data extraction attack.
  
  Each instance in this scenario contains
  
  1. a randomly sampled prefix from the bookcorpus, and
  2. the entire remaining book.
  
  Methodology adapted from
      Carlini, Nicholas, et al.
      "Extracting training data from large language models."
      30th USENIX Security Symposium (USENIX Security 21). 2021.
code: def get_instances(self) -> List[Instance]:
      target_path: str = os.path.join(
          self.output_path, f"{self.datatag}.json"
      )
      ensure_file_downloaded(self.source_url, target_path)
  
      with open(target_path, "r") as f:
          # Processed data with the format {"data": {prefix: prefix_to_end}, "metadata":...}.
          data = json.load(f)
  
      # Read all the instances
      instances: List[Instance] = []
      for prefix, prefix_to_end in tqdm.tqdm(
          data["data"].items(), desc="load instances", disable=None
      ):
          instances.append(
              Instance(
                  input=Input(text=prefix),
                  references=[
                      Reference(
                          Output(text=prefix_to_end), tags=[CORRECT_TAG]
                      )
                  ],
                  split=TEST_SPLIT,
              ),
          )
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/copyright_scenario.py
imports: import json
  import os
  import tqdm
  from typing import List
  from helm.common.general import ensure_file_downloaded
  from .scenario import Scenario, Instance, Reference, CORRECT_TAG, TEST_SPLIT, Input, Output
label_desc: Use image node_id 2 for calling the CopyrightScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances